RECEIVE JAYWING'S RISK INSIGHTS

Fraud contamination in credit risk models: why portfolios stop behaving as expected

Fraud losses hiding inside credit risk models corrupt training data for years. Here's what a retrospective audit catches that backtesting can't.

FCA CP 26/7 and CRA reform: How will it impact credit risk models

CP26/7 makes multi-bureau credit reporting mandatory. Here's what credit risk teams should check now, before it changes who your models approve and decline.

15 mins with…Sally Felton. Lessons learned from 30 years in fraud risk

In this interview, we asked Sally what 30 years have taught her about where firms are still exposed. Here are her top six lessons.

Modernising regulated analytics (without rebuilding legacy controls)

Why analytics modernisation stalls when governance stays the same in regulated financial services.

15 mins with…Bilal Shaik Mohammad, Director of Sales, GCC

Combined vs bespoke models in credit risk: Does segmentation still add value?

Do segmented models improve credit risk performance? New analysis shows why combined ML models consistently outperform segmented approaches.

Hybrid case-based reasoning for better underwriting decisions

How machine learning and generative AI improve manual underwriting through better case selection, decision support and consistency.

Modernising credit-risk models without drifting outside risk appetite

Modernising credit risk models: how lenders can introduce explainable ML without weakening governance or drifting outside risk appetite.

Identifying hidden fraud networks: Why fraud detection needs a network-based approach

Fraud is now networked. Learn how graph databases help detect fraud rings, reduce losses and improve real-time decision making.